Year: 2024
Type: article
Abstract: This review explores the various methods employed in detecting leaf diseases, highlighting their significance in ensuring healthy crop yields. The paper examines traditional techniques like visual inspection and microscopy, as well as advanced methods that leverage machine learning, image processing, and deep learning algorithms. Emphasis is placed on the accuracy, efficiency, and scalability of these approaches, considering the challenges of real-time disease detection in diverse environmental conditions. By comparing the strengths and limitations of different methods, the review aims to provide a comprehensive understanding of the current state and future directions in leaf disease detection technology.
Authors: Dr. Sheshang Degadwala, Dhairya Vyas
Institution: Sigma University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4401905488